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Etymologie

Was (wer) ist finite quantification - definition

Branching quantification; Henkin quantifier; Partially ordered quantification; Branched quantification

Finite morphism

In algebraic geometry, a finite morphism between two affine varieties X, Y is a dense regular map which induces isomorphic inclusion k\left[Y\right]\hookrightarrow k\left[X\right] between their coordinate rings, such that k\left[X\right] is integral over k\left[Y\right]. This definition can be extended to the quasi-projective varieties, such that a regular map f\colon X\to Y between quasiprojective varieties is finite if any point like y\in Y has an affine neighbourhood V such that U=f^{-1}(V) is affine and f\colon U\to V is a finite map (in view of the previous definition, because it is between affine varieties).

https://en.wikipedia.org/wiki/Finite_morphism

Finite element method

$Solving the two-dimensional problem <math>u_{xx}+u_{yy}=-4</math> in the disk centered at the origin and radius 1, with zero boundary conditions.<br />(a) The triangulation.$

NUMERICAL METHOD FOR SOLVING PHYSICAL OR ENGINEERING PROBLEMS

Finite element analysis; Finite Element Analysis; Finite elements; Finite element; Finite Element Method; Engineering treatment of the finite element method; Finite element solver; Finite element meshing; Finite element problem; Engineering treatment of the Finite Element Method; Finite element methods; Finite difference method based on variation principle; Finite elements analysis; Finite-element method; Finite-element analysis; Finite-element methods; Nonlinear finite element analysis

The finite element method (FEM) is a popular method for numerically solving differential equations arising in engineering and mathematical modeling. Typical problem areas of interest include the traditional fields of structural analysis, heat transfer, fluid flow, mass transport, and electromagnetic potential.

https://en.wikipedia.org/wiki/Finite_element_method

Quantification (machine learning)

MACHINE LEARNING PRACTICE OF SUPERVISED LEARNING

Binary quantification; Multiclass quantification; Ordinal quantification

In machine learning and data mining, quantification (variously called learning to quantify, or supervised prevalence estimation, or class prior estimation) is the task of using supervised learning in order to train models (quantifiers) that estimate the relative frequencies (also known as prevalence values) of the classes of interest in a sample of unlabelled data items.

https://en.wikipedia.org/wiki/Quantification_(machine_learning)

Wikipedia

Branching quantifier

In logic a branching quantifier, also called a Henkin quantifier, finite partially ordered quantifier or even nonlinear quantifier, is a partial ordering

\langle Qx_{1}\dots Qx_{n}\rangle

of quantifiers for Q ∈ {∀,∃}. It is a special case of generalized quantifier. In classical logic, quantifier prefixes are linearly ordered such that the value of a variable y_m bound by a quantifier Q_m depends on the value of the variables

y₁, ..., y_m−1

bound by quantifiers

Qy₁, ..., Qy_m−1

preceding Q_m. In a logic with (finite) partially ordered quantification this is not in general the case.

Branching quantification first appeared in a 1959 conference paper of Leon Henkin. Systems of partially ordered quantification are intermediate in strength between first-order logic and second-order logic. They are being used as a basis for Hintikka's and Gabriel Sandu's independence-friendly logic.

https://en.wikipedia.org/wiki/Branching quantifier